Warn on empty POS for the rule-based lemmatizer

Add a warning to the rule-based lemmatizer for any tokens without POS
annotation.

spacy/errors.py

@@ -119,6 +119,10 @@ class Warnings:
             "call the {matcher} on each Doc object.")
     W107 = ("The property `Doc.{prop}` is deprecated. Use "
             "`Doc.has_annotation(\"{attr}\")` instead.")
+    W108 = ("The rule-based lemmatizer did not find POS annotation for the "
+            "token '{text}'. Check that your pipeline includes components that "
+            "assign token.pos, typically 'tagger'+'attribute_ruler' or "
+            "'morphologizer'.")
 
 
 @add_codes

spacy/pipeline/lemmatizer.py

@@ -4,7 +4,7 @@ from thinc.api import Model
 from pathlib import Path
 
 from .pipe import Pipe
-from ..errors import Errors
+from ..errors import Errors, Warnings
 from ..language import Language
 from ..training import Example
 from ..lookups import Lookups, load_lookups
@@ -197,6 +197,8 @@ class Lemmatizer(Pipe):
         string = token.text
         univ_pos = token.pos_.lower()
         if univ_pos in ("", "eol", "space"):
+            if univ_pos == "":
+                logger.warn(Warnings.W108.format(text=string))
             return [string.lower()]
         # See Issue #435 for example of where this logic is requied.
         if self.is_base_form(token):

spacy/tests/pipeline/test_lemmatizer.py

@@ -1,4 +1,6 @@
 import pytest
+import logging
+import mock
 from spacy import util, registry
 from spacy.lang.en import English
 from spacy.lookups import Lookups
@@ -54,9 +56,18 @@ def test_lemmatizer_config(nlp):
     lemmatizer = nlp.add_pipe("lemmatizer", config={"mode": "rule"})
     nlp.initialize()
 
+    # warning if no POS assigned
+    doc = nlp.make_doc("coping")
+    logger = logging.getLogger("spacy")
+    with mock.patch.object(logger, "warn") as mock_warn:
+        doc = lemmatizer(doc)
+        mock_warn.assert_called_once()
+
+    # works with POS
     doc = nlp.make_doc("coping")
-    doc[0].pos_ = "VERB"
     assert doc[0].lemma_ == ""
+    doc[0].pos_ = "VERB"
+    doc = lemmatizer(doc)
     doc = lemmatizer(doc)
     assert doc[0].text == "coping"
     assert doc[0].lemma_ == "cope"